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   "source": [
    "# Designing with Data\n",
    "by *Rochelle King; Elizabeth F Churchill; Caitlin Tan*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 1. Introducing a Data Mindset"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data as a Trend\n",
    "- big data / data is hot topic and currency\n",
    "- the ease of gathering data can lead us to be lazy in our thinking, resulting in erroneous conclusions if the data quality is low or unrepresentative or the data analysis is flawed\n",
    "- more potential than collecting data would be run experiments\n",
    "- no \"one-size-fits-all\" approach; different methods/techniques/constraints/pros&cons\n",
    "- A/B Test - methodology of collaboration between design & data\n",
    "- apply data or A/B Test to design process is rather creative, iterative, and empowering than worrying about ruin the design intuition"
   ]
  },
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3 Ways to Think about Data\n",
    "- data driven (a train on tracks: you know the approach you are taking, your approach is reliable, and what you are going to find out is understood, directed, predetermined, and repeatable)\n",
    "- data informed (a train station: you know there are various trains, and they are likely going to different places. You are aware there are options and there are mechanisms for finding out which is the right train for you)\n",
    "- data awareness (Mapping, transportation & navigation: trains are just one method of getting around the landscape, and there is a whole world available to explore)\n",
    "\n",
    "Data-Aware vs Instinct-Driven\n",
    "- data aware: fit for purpose\n",
    "- instinct driven: experimental\n",
    "\n",
    "Require a balance when designing and making decisions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Data & Design & Business\n",
    "- data and design are both tools of problem solving to make your business better\n",
    "- data can help align your design with your business goals\n",
    "- data has no conflits with design\n",
    "\n",
    "2 roles about data in company:\n",
    "- data producer\n",
    "- data consumer\n",
    "\n",
    "A single individual might play both roles."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Chapter 2. The ABCs of Using Data\n",
    "- ultimate design goal: to build the best possible experiences for your users\n",
    "- basic principles of experimentation\n",
    "- how to apply experimentation(AB Testing) to Internet product design\n",
    "- necessary role of creativity in A/B testing"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### The Diversity of Data\n",
    "no one best type of data, the best data is helping you learn your topic/question\n",
    "\n",
    "#### Why\n",
    "1. users’ behaviors (collecting behavioral data / actions)\n",
    "2. users' attitudes & expectations ( “affect” data reflects emoitonal state)\n",
    "\n",
    "Compared to behavioural data, attitudes and emotions can be harder to measure.\n",
    "\n",
    "Social Desirability Response / Acquiescence Bias: \n",
    "\n",
    "users often want to give the “right answer,” so they’ll tell you what they think you want to hear, rather than what they actually believe\n",
    "\n",
    "#### When\n",
    "\n",
    "longitudinal --> video stream\n",
    "\n",
    "snampshot --> photograph\n",
    "\n",
    "1. longitudinal: \n",
    "- from the same user over a period of time, allowing you to see how users change, adapt, and learn\n",
    "- wait until the period of time is over, your data inevitably will take longer to collect\n",
    "\n",
    "2. snapshot\n",
    "- observe just one instance interacting with your product\n",
    "- much quicker to collect\n",
    "\n",
    "whether to collect data contextually or in isolation depends on your need\n",
    "\n",
    "\n",
    "#### How\n",
    "\n",
    "Data Types:\n",
    "\n",
    "1. Qualitative Data\n",
    "- uses narrative to answer questions\n",
    "- help build empathy for users\n",
    "- inform your understanding of users' attitudes, beliefs, values, and needs\n",
    "\n",
    "2. Quantitative Data\n",
    "- expresses observations through numbers and measurement\n",
    "- measure the impact to certain metrics (e.g. DAU:daily active users & URR:user retention rates)\n",
    "\n",
    "Collect data through:\n",
    "1. self-report (asking users to answer questions)\n",
    "2. observation (watching the user’s actions or behaviours)\n",
    "\n",
    "\n",
    "1. moderated data (e.g. interviews;)\n",
    "2. unmoderated data (e.g. surveys;)\n",
    "\n",
    "#### How Much\n",
    "- identify more than 85% of usability issues with only five participants\n",
    "- data from more participants would have more precise information\n",
    "- statistical method to determine a degree of confidence"
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